def _get_input_from_iterator(iterator, with_batch_index=False):
"""Get elements from the iterator and verify the input shape and type."""
next_element = next(iterator)
if with_batch_index:
batch_index, next_element = next_element
else:
batch_index = None
if (tensor_util.is_tensor(next_element)
or isinstance(next_element,
(dict, composite_tensor.CompositeTensor))):
next_element = [next_element]
if len(next_element) == 1:
x, = next_element
y = None
sample_weights = None
elif len(next_element) == 2:
x, y = next_element
sample_weights = None
else:
x, y, sample_weights = next_element
# Validate that all the elements in x and y are of the same type and shape.
dist_utils.validate_distributed_dataset_inputs(
distribution_strategy_context.get_strategy(), x, y, sample_weights)
return x, y, sample_weights, batch_index
def test_validating_dataset_input_tensors_with_dtype_mismatch(
self, distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.float64)
x = values.DistributedValues((a, b))
y = values.DistributedValues((a, a))
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegexp(
ValueError, 'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def test_validating_dataset_input_tensors_with_dtype_mismatch(
self, distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.float64)
device_map = values.ReplicaDeviceMap(('/device:CPU:0', '/device:GPU:0'))
x = values.DistributedValues(device_map, (a, b))
y = values.DistributedValues(device_map, (a, a))
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegexp(
ValueError, 'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
